Lindahl Lecture 3: Housing, Transportation Technology and City Governments Edward L. Glaeser Harvard University.

Lindahl Lecture 3: Housing, Transportation Technology and

City GovernmentsEdward L. Glaeser

Harvard University

Structure of Lecture

• Transportation Technologies and Cities– Urban Poverty – Sprawl

• Housing Demand and Supply• Government Policies towards Housing

– Rent Control– Subsidized Homeownership– General Aside on Social Capital

• Cities and Governments

Why do the poor live in central cities?

• Poverty rate in central cities is 18%; in suburbs it is 8%

• In old metropolitan areas, poverty frises and then falls with distance from CBD

• In newer metro areas, poverty just declines with distance from CBD

• New migrants to cities are just as poor as old residents– selection not treatment

The AMM Model

• With two groups the willingness to pay for proximity per acre determines who lives closer to the city center

• The key willingness to pay comes is P’(d) from P(d)a+dt= total costs, so willingness to pay is for proximity is -P’(d)=t/a

• This means that the poor live in cities if they have higher commuting costs or less demand for land

The Model Graphically

House Price

Distance

Whoever has a steeper curveLives near the center

The Elasticity Condition

• With two groups, the question is whether tr / ar >tp / ap or

or or

pr

pr

p

p

pr

pr

p

p

p

pr

p

pr

p

r

p

r

yy

aa

a

y

yy

tt

t

y

a

aa

t

tt

a

a

t

t

Poor in Cities continued

With income as a continuum, the key question is whether the income elasticity of demand for land is greater or less than the income elasticity of commuting costs.

What is a reasonable benchmark for the income elasticity of commuting costs? One mode– probably somewhat under 1.

The Income Elasticity of Demand for Land

• If you just look at people live in single family detached houses: .1

• This rises to .3 if you instrument with education for income (perm. Income)

• But apartments are the critical issue, and then you need to assign land to an apartment.

• Our best estimate is .3-.4.

Why do the poor live in cities?

• The role of transport modes– if the rich drive and the poor take transit the puzzle can be resolved.

• Even though the rich pay more for driving the marginal cost of distance is less

• Cars move (on average) at 30 mph and have a minute fixed cost

• Buses move at under 20 on average• The income elasticity of auto ownership is high

Evidence on the Role of Public Transportation

• Cross-section– people who live close to public transportation are poorer holding distance to CBD fixed– Rail in Boston, Portland, Washington– Buses in LA– Subways in NYC (outer buroughs)

• Panel– when tracts get new access to trains, the poverty rate rises

Evidence continued

• In areas where everyone drives, the rich live closer to the city

• The existence of subways creates a zone where the rich take public transport and dense cities the rich walk in the center– In these cities, the relationship between

income and distance is not monotonic

• Statistically, these subways change the urban form

Why the 20th century transformation?

• The move to sun and sprawl both reflect the same phenomenon.

• Transportation costs have fallen.

• Consumer cities– not producer cities.

• Car cities, not walking or PT cities.

Reduction of the Costs of Moving Goods

• Railroads, Trucking, Highways have radically reduced transport costs.

• Manufacturing no longer locates near its suppliers/consumers.

• Manufacturing has suburbanized and left cities (and the US) altogether.

• Boston was typical– not unique.

Declining Transport Costs: RailDollars per ton-mile (real)

Year1890 2000

0.023

0.185

Dollars per ton-mile (real)

Year1890 2000

0.023

0.185

Declining Costs: More Modes

Transportation share of GDP Without air

1850 1900 1950 2000

0.02

0.04

0.06

0.08

0.10

Year

Share of GDPin transport

Transportation share of GDP Without air

1850 1900 1950 2000

0.02

0.04

0.06

0.08

0.10

0.02

0.04

0.06

0.08

0.10

Year

Share of GDPin transport

As Transport Costs Fell– Manufacturing Left Cities

• First is suburbanized– Manufacturing firms are big users of space – There is a strong tendency of these firms to locate for

from the city center. • Then it left high density counties

• And it left the U.S.

• There is no reason to think that this is inefficient or bad.

Manufacturing and Density

Log of people per square mile

Manufacturing share

-1.84 11.08

-0.02

0.54

Log of people per square mile

Manufacturing share

-1.84 11.08

-0.02

0.54

Manufacturing and Decline 1920-1980

Figure 9: Manufacturing and Urban DeclineManufacturing Employment Share

Population Grow th 20-80 .

0 20 40

-1

0

1

2

3

SAN DIEG

LOS ANGE

DULUTH,

SIOUX CI

NEW ORLE SALT LAKDES MOIN

MANCHEST

TACOMA, SPOKANE,

KNOXVILL

FLINT, M

WATERBUR

FALL RIV

BIRMINGH

WICHITA,

HOUSTON,

SPRINGFI

PORTLAND

DENVER,

ST. PAUL

SAN ANTO

SOUTH BEOAKLAND,

ERIE, PA

WORCESTE

YONKERS,

OMAHA, N

YOUNGSTO

OKLAHOMA

SEATTLE,

FORT WAY

KANSAS C

CINCINNAUTICA, N

ST. JOSE

JACKSONV

ALBANY, NEW BEDF

KANSAS C

EL PASO,

FORT WORNASHVILL

TROY, NY

MEMPHIS,

DALLAS,

CANTON,

SCRANTON

RICHMOND

INDIANAP

WASHINGT ALLENTOW

ATLANTA,

MINNEAPO

GRAND RA

PEORIA, TOLEDO,

LOWELL,

HARTFORD

NEW HAVE

AKRON, O

TULSA, O

BALTIMORSYRACUSE

DAYTON, EVANSVIL

BRIDGEPOELIZABET

ROCHESTE

LOUISVIL

COLUMBUS

READING,SCHENECT

SAVANNAH

SAN FRAN

ST. LOUI

DETROIT,

HARRISBU BUFFALO, PROVIDEN

CHICAGO,

LAWRENCECLEVELANWILKES-B

PHILADEL

PITTSBUR CAMDEN,

NORFOLK,

WILMINGT

PATERSON

TRENTON,BOSTON, CAMBRIDG

NEWARK,

MILWAUKENEW YORK

BAYONNE,JERSEY C

SOMERVIL

The Rise of Car Cities

• First, there was flight to the suburbs.

• Then the jobs left too– Now more than 75 percent of Chicago’s jobs are outside the classic downtown.

• Firms followed people (again consumer cities).

• Movement both within MSAs to edge cities and across MSAs to car friendly places.

Density and Decline: 1920-1980

Figure 8: Density and City Growth 1920-1980dens20

Population Grow th 20-80 Fitted values

947.754 23869.5

-.545373

2.46159

NEW YORKCHICAGO,

PHILADEL

DETROIT,

CLEVELAN

ST. LOUI

BOSTON,

BALTIMOR

PITTSBUR

LOS ANGE

BUFFALO,

SAN FRAN MILWAUKEWASHINGT

NEWARK,

CINCINNA

NEW ORLE

MINNEAPO

KANSAS CSEATTLE,

INDIANAP

JERSEY CROCHESTE

PORTLAND

DENVER,

TOLEDO,

PROVIDEN

COLUMBUS

LOUISVILST. PAUL

OAKLAND,

AKRON, O

ATLANTA,

OMAHA, N

WORCESTE

BIRMINGH

SYRACUSE

RICHMOND

NEW HAVE

MEMPHIS,

SAN ANTO

DALLAS,

DAYTON,

BRIDGEPO

HOUSTON,

HARTFORD

SCRANTON

GRAND RA

PATERSON

YOUNGSTO

SPRINGFI

DES MOIN

NEW BEDFFALL RIV TRENTON,

NASHVILL

SALT LAK

CAMDEN,

NORFOLK,

ALBANY, LOWELL,

WILMINGT

CAMBRIDG

READING,

FORT WOR

SPOKANE, KANSAS C

YONKERS,

DULUTH,

TACOMA,

ELIZABET

LAWRENCE

UTICA, N

ERIE, PA

SOMERVIL

WATERBUR

FLINT, M

JACKSONV

OKLAHOMA

SCHENECT

CANTON,

FORT WAY

EVANSVILSAVANNAH

MANCHEST

ST. JOSE

KNOXVILL

EL PASO,

BAYONNE,

PEORIA,

HARRISBU

SAN DIEG

WILKES-B

ALLENTOW

WICHITA,

TULSA, O

TROY, NY

SIOUX CI

SOUTH BE

Figure 8: Density and City Growth 1920-1980dens20

Population Grow th 20-80 Fitted values

947.754 23869.5

-.545373

2.46159

NEW YORKCHICAGO,

PHILADEL

DETROIT,

CLEVELAN

ST. LOUI

BOSTON,

BALTIMOR

PITTSBUR

LOS ANGE

BUFFALO,

SAN FRAN MILWAUKEWASHINGT

NEWARK,

CINCINNA

NEW ORLE

MINNEAPO

KANSAS CSEATTLE,

INDIANAP

JERSEY CROCHESTE

PORTLAND

DENVER,

TOLEDO,

PROVIDEN

COLUMBUS

LOUISVILST. PAUL

OAKLAND,

AKRON, O

ATLANTA,

OMAHA, N

WORCESTE

BIRMINGH

SYRACUSE

RICHMOND

NEW HAVE

MEMPHIS,

SAN ANTO

DALLAS,

DAYTON,

BRIDGEPO

HOUSTON,

HARTFORD

SCRANTON

GRAND RA

PATERSON

YOUNGSTO

SPRINGFI

DES MOIN

NEW BEDFFALL RIV TRENTON,

NASHVILL

SALT LAK

CAMDEN,

NORFOLK,

ALBANY, LOWELL,

WILMINGT

CAMBRIDG

READING,

FORT WOR

SPOKANE, KANSAS C

YONKERS,

DULUTH,

TACOMA,

ELIZABET

LAWRENCE

UTICA, N

ERIE, PA

SOMERVIL

WATERBUR

FLINT, M

JACKSONV

OKLAHOMA

SCHENECT

CANTON,

FORT WAY

EVANSVILSAVANNAH

MANCHEST

ST. JOSE

KNOXVILL

EL PASO,

BAYONNE,

PEORIA,

HARRISBU

SAN DIEG

WILKES-B

ALLENTOW

WICHITA,

TULSA, O

TROY, NY

SIOUX CI

SOUTH BE

Car Cities in the 1990s: Is there a New Urbanism?

avg. vehicles available per hhol

growth Fitted values

.6 2.3

-.129964

.852277

Facts about Sprawl

• In most American cities, more than 80 percent of people live more than 3 miles from the CBD

• More than 75 percent of workers work outside that ring

• In cities with decentralized employment, rents don’t rise much with distance and commute times don’t rise

Sprawl Cities are Car Cities

• 92 percent of trips are by car• Even 77 percent of trips under a mile are

by car• Places with more African-Americans in the

center have more dispersion– but the differences are small

• Across countries, using gas prices instrumented for by legal origin predicts sprawl

Is Sprawl Bad?

• Pollution– potentially serious for global warming, but most other problems have been taken car of by technology

• Little land area is actually used in the U.S.• Congestion– sure, but it is not obvious that

congestion rises with sprawl– commute times actually fall

• Commute time by car is 23 minutes on average 47 minutes by public transportation

• The big plus is housing size which reached over 2000 square feet in the last few years

Cars and Driving Times

% using public transportation 19

average travel time to work (mi Fitted values

.2 53.4

17

36.8513

Albuquer

Anaheim

Arlingto

Atlanta

Austin c

Baltimor

Birmingh

Boston c

Buffalo

Charlott

Chicago

Cincinna

Clevelan

Colorado

Columbus

Corpus C

Dallas c

Denver c

Detroit

El Paso

Fort Wor

Fresno c

Honolulu

Houston

IndianapJacksonv

Kansas CLas Vega

Long Bea

Los Ange

Louisvil

Memphis

Mesa cit

Miami ci

MilwaukeMinneapo

Nashvill

New Orle

New York

Newark c

Norfolk

Oakland

Oklahoma

Omaha ci

Philadel

Phoenix

PittsburPortland

Sacramen

San Anto

San Dieg

San Fran

San Jose

Santa An

Seattle St. Loui

St. PaulTampa ci

Toledo c

Tucson c

Tulsa ci

Virginia

Washingt

Wichita

The Europeans and their Trains

• Fact # 1: In rich European cities, people now drive just like in the U.S.

• Fact # 2: In many cities where people rarely drive, commute times are very high:– Moscow 10% drive, 62 minute commute.– Athens 36% drive, 53 minute commute.– Paris 60% drive, 35 minute commute. – US average is 24 minutes.

Cars and Travel Time Internationally

% wrk trips by private car

mean travel time to work (mins) Fitted values

2 81

14

78

Yerevan

Melbourn

Gaborone

Brasilia

Curitiba

Recife

Rio de J

Bandar S

Sofia

Toronto

Santiago

Brazzavi Zagreb

Camaguey

La Haban

Prague

Copenhag Djibouti BordeauxDunkerqu

Paris

RennesStrasbou

Tbilisi

Athens

Budapest

Amman

Almaty

RigaVilnius

Birkirka

ChisinauAmsterda

Buchares

Kostroma

Moscow

Nizhny N

Novgorod

Ryazan

Sao Tome

BratislaBratisla

Koper

Ljubljan

Maribor

Stockhol

Dubai

Hertford

New YorkBelgrade

Nis

Novi Sad

Housing Demand and Supply

• Traditionally urban literature has focused on housing demand– using housing price hedonics to back out demand for place

• New literature focuses more on supply, in part because supply drives city growth

• In part because recent regulatory changes are incredibly important and underexplored

Why Supply MattersLo

g H

ous

ing

Uni

ts, 2

000

Figure 1: Housing Units and Population Levels, 2000Log Population, 2000

log total housing units 2000 Fitted values

10.0819 15.896

9.25387

14.9789

JohnstowAnnistonNew LondPoughkeeWilliams

WheelingJamestowHouma ciParkersbTexarkan

FlorenceJackson HagerstoBremerto

State Co

Wausau cFitchburLima citMuskegonAtlanticYork citMiddletoBurlingt

CharlottJoplin cAlexandrBinghamtDanville

New Brun

HarrisbuMansfielAltoona Biloxi cHuntingtElkhart SarasotaMonroe cBattle CCharlestDecatur Galvesto

Santa CrJohnson Niagara GreenvilPensacolLancasteLafayettDothan cFayettevJanesvilTerre HaAndersonUtica ciHamiltonEau ClaiSaginaw Santa FeDaytona PortlandBloomingLynchburBryan ci

BellinghMuncie cChampaigLorain cWaterlooAshevill

BloomingAppletonMelbournLake ChaYakima cLawrenceWilmingtPawtuckeLongviewLas CrucDanbury

WilmingtScrantonGreeley Albany cKalamazo

Santa Ma

TuscalooLakelandFort SmiCanton cReading Racine cDecatur YoungstoWest PalNorwalk Tyler ciColumbiaSioux CiTrenton RochesteKilleen Duluth cSan AngeBillingsKenosha

Fargo ciOdessa cFall RivSanta BaLawton cNew BedfBrocktonBoulder

Roanoke Midland GainesviAlbany cCharlestMacon ciDavenporLivonia PortsmouPueblo cBerkeleyGary citClarksvi

Erie citWichita

Provo ci

Lowell cJoliet cMcAllen AllentowManchestWaterburSouth BeLafayettSimi Val

Springfi

InglewooPeoria cIndependWaco citBeaumontAnn ArboAbilene

El Monte

ColumbiaVallejo ThousandStamfordFort ColLansing Elizabet

Cedar RaFayettevHartfordEvansvilConcord Topeka cNew HaveSioux FaSterlingMesquite

Flint ciFullerto

Alexandr

Orange c

SavannahSunnyvalEscondidPasadenaSalem ciEugene cTorranceWarren cHollywooBridgepoBrownsviHayward PasadenaAurora cLakewoodHampton Kansas CSyracuseSanta RoOverland

PatersonPomona c

RockfordTallahas

Salinas

SpringfiSpringfi

Fort LauChattano

Ontario

HuntsvilTempe ciOceansidGarden G

Dayton c

Oxnard c

WorcesteChula ViProvidenAmarilloKnoxvill

Laredo c

Newport Reno citSalt LakLittle RJackson San BernWinston-Boise CiOrlando

ColumbusDurham cModesto HuntingtIrving cTacoma cGlendaleSpokane Yonkers RichmondGrand RaDes MoinMobile cChesapeaLubbock ShrevepoMontgomeScottsda

Fremont

Fort WayMadison Garland Akron ciGlendaleRochesteGreensboLincoln

Hialeah

Baton RoNorfolk Jersey CBirmingh

StocktonBakersfi

St. Pete

Riversid

LouisvilLexingtoNewark cRaleigh Aurora cCorpus CSt. Paul

Buffalo Tampa ciToledo c

Anaheim

CincinnaArlingtoPittsbur

Santa An

Wichita St. LouiColoradoMiami ciHonoluluMinneapoOmaha ciTulsa ciMesa cit

Oakland SacramenAtlanta VirginiaFresno c

Kansas CAlbuquerLong BeaClevelanLas VegaNew OrleTucson cOklahomaPortland

Fort WorCharlottDenver cSeattle

El Paso Nashvill

WashingtBoston cMilwaukeMemphis BaltimorAustin cColumbusJacksonv

San FranIndianapSan Jose

Detroit San Anto

Dallas cSan DiegPhoenix

PhiladelHouston

Chicago Los Ange

New York

And in changesLo

g C

han

ge

Ho

usin

g U

nits

, 19

70-2

000

Figure 3: Log Changes in Housing Units and Population, 1970-2000Log Change Population, 1970-2000

log change tot housing units, 1 Fitted values

-.580574 1.84035

-.344024

2.1741

St. Loui

JohnstowGary citYoungsto

Detroit

Buffalo ClevelanPittsburFlint ciNiagara WheelingUtica ciSaginaw

Dayton cHuntingtLouisvil

Newark c

Baltimor

HarrisbuCincinnaCanton cScrantonBinghamtRochesteSyracuseCharlestParkersbLima citWashingtNorfolk

Hartford

AnnistonWarren c

PhiladelAltoona Akron ciRichmondMacon ciJamestowJackson

Erie citBirminghWilliamsYork cit

New Lond

Trenton New OrleToledo cAlbany cMilwauke

Atlanta Anderson

Atlantic

Terre HaSouth BeRacine c

Chicago Duluth cKansas CKansas CLorain cEvansvil

Galvesto

BerkeleyMinneapoPeoria cBridgepo

MansfielHamiltonNew HaveMuskegon

KalamazoFitchburDecatur WilmingtLansing PortsmouWaterloo

Livonia Greenvil

Boston c

Lake Cha

Jersey CNew BedfSt. PaulReading SpringfiPoughkeeMonroe cPensacolPawtuckeFall RivYonkers Providen

Allentow

Muncie cWorcesteLancaste

Topeka cBeaumontPortland

Sioux CiDes MoinWaterburKnoxvill

DavenporGrand RaNew York

IndependRockfordHagersto

Columbia

Torrance

Paterson

Roanoke

Salt Lak

Memphis Danville

Biloxi c

Mobile cNorwalk Pueblo cHouma ciBremerto

Brockton

Albany c

Seattle

Florence

Elizabet

Stamford

Lawrence

Denver cMiami ciWichita

San Fran

Cedar RaFort LauTampa ciShrevepo

Oakland

Savannah

Lowell cAlexandrOmaha ciState Co

TexarkanAnn ArboHonoluluKenosha Spokane

Huntsvil

St. Pete

AlexandrFort Way

CharlottJoplin c

Odessa c

New Brun

Middleto

Wausau c

Pasadena

Tuscaloo

Tulsa ciElkhart

Champaig

Waco cit

AshevillJackson Madison LynchburHampton SpringfiManchestAppletonBurlingt

Lawton cWichita

Inglewoo

Tacoma c

LafayettSpringfiFort SmiJanesvil

Long Bea

Abilene ChattanoHollywoo

Newport

Sarasota

Los AngeSanta Ba

ColumbusLubbock

Garden G

Joliet c

Corpus CFort WorAmarilloBattle C

Baton RoOklahoma

Little REau ClaiSunnyvalSan AngePortland

Winston-

Dallas cDaytona Boulder Decatur Concord West Pal

ColumbiaCharlestTyler ciBillings

Fullerto

Glendale

Danbury

Gainesvi

Hayward

Lincoln Montgome

Santa FeGreensboLakewoodDothan c

Yakima c

Houston PasadenaMidland Rocheste

Lafayett

SacramenLongview

Blooming

BloomingHuntingt

Johnson Wilmingt

El Monte

Orange c

Fargo ci

Santa Cr

Bellingh

Pomona c

Sioux FaEl Paso San AntoSan DiegVallejo

Melbourn

San Bern

Eugene c

Riversid

AlbuquerTucson cOrlando Fayettev

Aurora c

Overland

Bryan ci

Durham cLas CrucSimi ValIrving c

Anaheim

Greeley Provo ci

San JoseSalem ci

Fremont

Tallahas

Santa An

Hialeah

ChesapeaCharlottMesquiteFayettev

Stockton

Phoenix

Raleigh

Santa MaOxnard c

Killeen

Ontario

Virginia

Reno citBoise Ci

Tempe ci

Chula ViLaredo c

Salinas

Fresno c

Austin cGarland BrownsviColorado

Fort ColMcAllen

Santa Ro

Scottsda

Modesto

ThousandClarksviBakersfi

Escondid

ArlingtoAurora cLas Vega

Oceansid

Glendale

Mesa cit

Vacancy Rate Coefficient is .1C

ha

ng

e in

Va

cancy

Ra

te, 19

90

-2000

Figure 4: Changes in Population and Vacancy Rates, 1990-2000log change population, 1990-2000

abs change in vacancy rate, 199 Fitted values

-.162849 .616416

-7.47307

7.59984

Johnstow

Youngsto

Hartford

St. Loui

Lima citGary cit

Utica ciBaltimor

Flint ci

Saginaw

Buffalo

Binghamt

Norfolk

SyracuseNiagara

New Lond

Wheeling

PittsburCincinna

Anniston

Macon ciDanville

Dayton c

Birmingh

Jamestow

Galvesto

Detroit

Charlest

Scranton

Harrisbu

Lansing Jackson New Bedf

Huntingt

Toledo c

AlexandrWashingt

ClevelanAlbany c

New Have

Rocheste

Fitchbur

Muncie c

Milwauke

Louisvil

Erie cit

Warren c

Altoona Savannah

PhiladelCanton c

Greenvil

Kalamazo

Williams

Evansvil

Trenton

Lorain c

Pensacol

Monroe c

Portsmou

York cit

SpringfiJackson

Racine c

Akron ci

Richmond

Bremerto

Mansfiel

New Orle

Decatur ParkersbKansas C

Waterbur

Roanoke

Bridgepo

Albany c

State Co

Lynchbur

Hamilton

Huntsvil

Fall Riv

Florence

Newark c

Peoria cMuskegon

Beaumont

Battle C

Livonia

Portland

Berkeley

Tuscaloo

Pawtucke

Anderson

Shrevepo

Independ

Middleto

Miami ci

Odessa c

Mobile c

Lancaste

Allentow

Kansas C

Wilmingt

Brockton

Duluth c

Lake Cha

Lowell c

Worceste

Honolulu

PasadenaChattano

Fort Lau

Topeka c

South Be

Boston c

Lawrence

Inglewoo

Des Moin

DavenporSarasotaWaterloo

Hagersto

Pueblo cDaytona Poughkee

Reading

Torrance

Wausau c

Terre Ha

Baton Ro

MinneapoSt. Pete

Chicago Ann Arbo

Little R

Yonkers

LongviewHuntingt

Grand Ra

San Ange

Jersey C

Knoxvill

St. Paul

Sioux Ci

Atlanta

Norwalk

Paterson

Springfi

Newport Los Ange

Midland

Houma ci

Champaig

Memphis

Atlantic

Appleton

Vallejo

Tulsa ci

Lubbock

San FranOakland

Manchest

Long Bea

RockfordMontgome

Dothan c

Corpus C

SpringfiSanta Ba

ProvidenVirginiaWichita Glendale

Stamford

Tampa ci

Eau Clai

Abilene

Madison

Seattle El Monte

Biloxi cNew York

Concord

El Paso

Hampton Tacoma c

Elizabet

Waco cit

Texarkan

Amarillo

San Dieg

Sacramen

Fort SmiFullerto

Spokane Decatur

BillingsTyler ci

Cedar Ra

Santa Fe

Joplin c

Simi ValSanta Cr

CharlottAshevill

Tempe ci

Thousand

Sunnyval

ColumbusKenosha

GainesviRiversid

Johnson

Orlando

San Bern

Wichita

Salt Lak

Boulder

Oklahoma

Lakewood

Burlingt

Danbury

Janesvil

Blooming

San Jose

Hollywoo

Modesto

Santa AnLawton c

Alexandr

Garden GStocktonOmaha ciOrange c

New BrunAlbuquer

Lafayett

Fremont

Lincoln

Dallas c

Columbia

Denver c

Ontario

Pasadena

Fort WayElkhart

Bryan ci

Garland Fort Wor

Las Cruc

Melbourn

Houston

Oxnard c

Tucson c

CharlestHialeah

Tallahas

Fresno cPortlandProvo ci

Rocheste

West Pal

GreensboFargo ci

San Anto

Columbia

Eugene c

MesquiteEscondid

Sioux Fa

Anaheim

Irving c

Aurora c

Blooming

Oceansid

Hayward Santa Ma

McAllen

Salem ci

Greeley

ArlingtoColorado

Chula Vi

Bellingh

Lafayett

Winston-Santa Ro

Yakima c

Chesapea

Raleigh

Overland

Phoenix

Reno cit

Fort Col

WilmingtCharlott

Killeen

Durham cClarksvi

Mesa cit

Fayettev

Joliet c

Salinas

Austin c

Brownsvi

BakersfiAurora c

Laredo c

Glendale

Boise Ci

Scottsda

Fayettev

Las Vega

Durable Housing: the Basic Idea

Durable housing is needed to explain American Cities

Me

dia

n ho

use

pric

e, $

200

0

Figure 2: Median Price Regression and Construction CostsFitted values

medval8000 Fitted values Construction Costs

18799.4 192187

18799.4

218594

Newark c

Buffalo

Providen

Dayton cSt. Loui

Atlanta ClevelanSyracuse

Boston cNorfolk

Detroit

SpringfiJersey C

RochesteLouisvilBaltimor

Philadel

Knoxvill

Cincinna

Birmingh

New York

Pittsbur

Spokane

Worceste

MinneapoSalt Lak

Akron ci

Chicago

El Paso RichmondGrand RaColumbusChattano

New Orle

Memphis

Kansas C

ColumbusMilwauke

St. Paul

Miami ci

Gary citSan Anto

Fort Way

Colorado

Flint ci

Long Bea

Des Moin

Omaha ciToledo c

Tampa ci

Denver c

St. Pete

Fresno c

Washingt

Kansas C

Jackson Tacoma c

Lexingto

Fort WorIndianap

Oakland

Tucson cLincoln

MontgomeNashvill

OklahomaLubbock ShrevepoMobile cGreensbo

PortlandAlbuquer

Little R

Jacksonv

Wichita

Madison Sacramen

Tulsa ciDallas c

Baton Ro

Austin c

Yonkers

Corpus C

Las Vega

Raleigh

Los Ange

VirginiaMesa cit

Phoenix

San Fran

Seattle

Santa An

San Dieg

Aurora c

Fort Lau

Riversid

Houston

Arlingto

Anaheim

San Jose

Implications of a Durable Housing Model

1. Population growth rates will be skewed because places grow quick and decline slowly.

2. There will be strong persistence of growth rates especially in decline

3. Places with housing costing below construction costs will not grow

4. Positive shocks increase population more than housing prices; negative shocks decrease housing prices more than population

5. Concave correlation between prices and growth

Results on Durable Housing

• Highly skewed distribution of growth rates• Coefficient of current growth on past growth is 1

if growth was negative and .4 if growth was positive

• Coefficient on price growth on population growth is 1.8 when negative and .2 when positive

• Strong relationship between housing below construction cost and no population growth

The Concave Price/Growth Relationship

Pric

e A

ppre

cia

tion,

197

0-2

000;

1=

100%

Figure 3: Price Appreciation and Urban GrowthPopulation Growth, 1970-2000

real median house price appreci Fitted values

-.44 0 1 2 3 4 5

-.28

0

1

2

3

4

St. Loui

JohnstowYoungstoGary citDetroit Buffalo ClevelanPittsbur

Flint ciNiagara Wheeling

Utica ciSaginaw

Dayton cHuntingt

LouisvilNewark cBaltimor

HarrisbuCincinnaCanton c

Binghamt

Scranton

RochesteLima citSyracuseParkersb

Washingt

Norfolk

Hartford

Anniston

Warren cPhiladel

Altoona

Akron ciRichmondBirminghMacon ci

Jamestow

CharlestJackson Erie citWilliamsNew Lond

York cit

Trenton

New OrleToledo cKansas CAlbany cMilwaukeMuncie c

Atlanta

AndersonSouth Be

Atlantic

Terre Ha

Mansfiel

Chicago

Racine cDuluth cKansas CEvansvilGalvestoLorain c

Berkeley

Minneapo

Peoria cBridgepoHamilton

New Have

Roanoke

MuskegonKalamazoFitchbur

Wilmingt

Decatur

Portsmou

Lansing Waterloo

Greenvil

Livonia

Boston c

Lake Cha

New Bedf

Jersey C

St. PaulReading SpringfiSpringfiPoughkeeAlexandrMonroe c

PensacolPawtucke

Fall Riv

Yonkers

BeaumontProvidenAllentowLancasteWorceste

Topeka cDes Moin

Portland

Sioux CiMemphis Waterbur

KnoxvillIndianap

Davenpor

Grand RaLynchbur

New York

IndependRockford

Pueblo cHagersto

Columbia

Torrance

Paterson

Salt Lak

BremertoDanville

Biloxi c

Norwalk

Mobile cHouma ci

Brockton

Albany c

Seattle

TexarkanFlorence

Elizabet

Stamford

Lawrence

Denver c

Wichita

Miami ci

San Fran

Omaha ci

Cedar Ra

Fort LauTampa ci

Shrevepo

Oakland

Savannah

Fort WayColumbus

Lowell cAshevill

Tuscaloo

Jackson Huntsvil

Odessa c

Ann Arbo

Honolulu

Kenosha

Spokane

St. Pete

Champaig

Alexandr

CharlottJoplin cNew BrunMiddletoState Co

Wausau c

Burlingt

Pasadena

Tulsa ciWaco citElkhart

Madison

Hampton

Manchest

AppletonWichita Lawton c

Inglewoo

Tacoma c

Lafayett

Nashvill

Fort SmiJanesvil

Long Bea

Abilene

Chattano

Newport HollywooSarasota

Los Ange

Santa Ba

ColumbusLubbock

Joliet c

Garden G

Charlest

Corpus CFort WorAmarilloBattle CBaton RoOklahoma

Little REau Clai

Portland

Sunnyval

San AngeWinston-Jacksonv

Tyler ciDallas cDaytona

Boulder

Decatur

Johnson

Billings

BloomingWest Pal

Concord

ColumbiaMontgome

Fullerto

Glendale

Danbury Lincoln

Gainesvi

Yakima c

Lexingto

Hayward Santa Fe

Greensbo

Longview

Lakewood

Houston Dothan c

Sacramen

PasadenaRochesteMidland

LafayettSan Anto

Blooming

Vallejo

HuntingtWilmingt

Springfi

El Monte

Orange c

Fargo ci

Santa Cr

Bellingh

Pomona c

Sioux Fa

Eugene c

El Paso

San Dieg

MelbournSan BernLakelandTucson c

RiversidAlbuquer

Orlando Fayettev

Salem ci

Aurora cOverland

San Jose

Bryan ci

Durham c

Las Cruc

Simi Val

Irving c

Anaheim

Greeley Provo ci

Fremont

SterlingTallahas

Fayettev

Santa AnBoise Ci

Hialeah Stockton

ChesapeaCharlott

Phoenix

Raleigh

Mesquite

Santa Ma

Clarksvi

Oxnard c

Killeen

Ontario

VirginiaReno citTempe ciBrownsviFresno c

Laredo c

Chula Vi

Salinas

Austin cColorado

Garland

Fort Col

McAllen

Santa Ro

Modesto

Scottsda

Thousand

Bakersfi

Escondid

Arlingto

Aurora c

Las Vega

Oceansid

GlendaleMesa cit

The Weather and Urban Growth

• We split the weather into positive shocks and negative shocks so that the same share is negative as had overall population declines

• The coefficient on weather and price growth is .006 for negative shocks and .002 for positive

• The coefficient on weather and population growth is .0008 for negative shocks and .068 for positive shocks

A Final Implication: Durable Housing and Poverty

• If cities decline by becoming less productive, and if productivity relates to skill level

• Then poor people will stay in declining cities disproportionately because they have cheap, durable housing

• Poor people do congregate in declining cities, but this disappears when you control for housing prices

The Regulatory Tax

• Housing Supply Costs, in growing areas, are C*S+R where C is structural cost, S is size of structure and R is residual

• In most of U.S. history, the 1970, R/(CS+R) is small– less than .2 almost everywhere

• Only over the past thirty years do prices start to greatly exceed construction costs

Why the gap between housing prices and construction costs?

• Theory # 1: Land is expensive

• Theory # 2: Regulation prevents new construction

• R=PL+T where P is land costs, L is land area and T is regulatory tax

• We don’t directly observe land costs, but we can estimate them hedonically

• R/L>10*the estimate of P– tax not land

Another piece of Evidence: NYC

• In New York City, apartments are always the cost of building up

• No matter what the fixed costs are, the marginal cost is technological and generally less than 200$/square foot

• Yet condo prices are now often over 600$/square foot

• Hard to reconcile with a free market

Other Evidence on Rising Regulation

• Declining numbers of permits

• Little correlation between prices and density across metro areas

• Correlation between changes in prices and changes in population has become negative across regions

• Places with more estimated “zoning tax” have other measures of regulation

The Change in the Price/Quantity Relationship

In NYC in the 50s and 60s, rising prices related strongly to new permits.

In the 80s and 90s, this positive relationship has disappear.

Anecdotal information strongly supports the idea that citizens groups can now block change, presumably to keep prices up.

We don’t know why this occurred.

Supply Restrictions and Urban Dynamics

• Any restrictions on new supply will change the way that cities develop.

• One possible source of restricted supply is zoning, but limited land is certainly another.

• This will change the ways cities develop– compare Massachusetts and Texas

Supply and Urban Growth

Price

Number of Homes

TexasSupply

MA Supply

Rise in Demand

Massachusetts Population

Figure 13: Price Growth and Schooling in TexasShare w /College Degrees 1980

Housing Price Grow th .

0 20 40 60

.329786

1.0161

Port Art

Laredo c

Pasadena

Brownsvi

Mesquite

Grand Pr

Harlinge

San Anto

Victoria

Odessa c

El Paso

Baytown

Corpus C

San Ange

Wichita Killeen

Amarillo

Waco cit

McAllen Longview

Fort Wor

BeaumontIrving cGalvesto

Temple c

North RiAbilene

Tyler ci

Garland

Dallas c

Lubbock

Houston

Bryan ci

Midland

Arlingto

Carrollt

Austin c

Denton c

Plano ci

Richards

College

Figure 13: Price Growth and Schooling in TexasShare w /College Degrees 1980

Housing Price Grow th .

0 20 40 60

.329786

1.0161

Port Art

Laredo c

Pasadena

Brownsvi

Mesquite

Grand Pr

Harlinge

San Anto

Victoria

Odessa c

El Paso

Baytown

Corpus C

San Ange

Wichita Killeen

Amarillo

Waco cit

McAllen Longview

Fort Wor

BeaumontIrving cGalvesto

Temple c

North RiAbilene

Tyler ci

Garland

Dallas c

Lubbock

Houston

Bryan ci

Midland

Arlingto

Carrollt

Austin c

Denton c

Plano ci

Richards

College

Massachusetts Prices

Figure 15: Price Growth and Schooling in Mass.Share w /College Degrees 1980

Housing Price Grow th .

0 20 40 60

1.00418

1.8703

New Bedf

Fall Riv

Lawrence

Taunton

Chicopee

Lynn cit

BrocktonLowell c

HaverhilMalden c

Springfi

Medford

Worceste

Quincy c

Somervil

Waltham

Boston c

Cambridg

Newton c

Texas Prices

Figure 13: Price Growth and Schooling in TexasShare w /College Degrees 1980

Housing Price Grow th .

0 20 40 60

.329786

1.0161

Port Art

Laredo c

Pasadena

Brownsvi

Mesquite

Grand Pr

Harlinge

San Anto

Victoria

Odessa c

El Paso

Baytown

Corpus C

San Ange

Wichita Killeen

Amarillo

Waco cit

McAllen Longview

Fort Wor

BeaumontIrving cGalvesto

Temple c

North RiAbilene

Tyler ci

Garland

Dallas c

Lubbock

Houston

Bryan ci

Midland

Arlingto

Carrollt

Austin c

Denton c

Plano ci

Richards

College

Texas Population

Figure 12: City Growth and Schooling in TexasShare w /College Degrees 1980

Population Grow th .

0 20 40 60

-.119038

1.12156

Haltom C

Port Art

Laredo c

Texas Ci

Pasadena

Brownsvi

Mesquite

Del Rio

Grand Pr

Harlinge

Texarkan

San Anto

Victoria

Odessa c

El Paso

Baytown Corpus CSan Ange

Wichita

Killeen

AmarilloLufkin cWaco cit

McAllen

Longview

Fort Wor

Beaumont

Sherman

Irving c

Galvesto

Temple c

North Ri

Kingsvil

Abilene Tyler ci

Garland

Hurst ci

Dallas cDuncanvi

Lubbock

Houston

Nacogdoc

Bryan ci

Midland

Arlingto

Carrollt

Austin c

Denton c

Plano ci

Richards

College

Responses to Labor Demand Shocks

Regulation (Wharton Survey)

Change in Population

Change in Income

Change in Prices

Low Regulation

1.04

(.3)

11597

(3917)

54899

(37478)

High Regulation

.2

(.3)

34651

(13007)

204730

(137972)

The Costs of Rent Control

• Undersupply• Reduced maintenance• Social waste on rent seeking• Misallocation (Deacon and Sonstelie,

Hubert, Suen)• Nat Sherman rented a six month CPW

apartment for 335/month and said the apartment “happens to be used so little that I think [the rent is] fair”

Misallocation under Rent Control

Rent

Transfer

SurplusLeft

DWL

Quantity

Demand

Supply

Misallocation under Rent Control

Rent

Transfer

SurplusLeft DWL

Quantity

a

b

c

ab=bc

MisallocationLoss

Demand

Supply

How Big is the Misallocation Loss?

• The misallocation loss is technically first order while the undersupply loss is second order

• Thus for sufficiently mild impositions of rent control the social loss is always greater from misallocation

• This relies on random matching– better matching would reduce losses

• Different impacts of demand elasticity --

Empirical Approach

• Assume that if household A consumes more of attribute y than household B in city 1, this will also be true in city 2.– This assumes the we can rank households by

demand.

• For any city c and subgroup i, the distribution of demand, f(d, x) equals f(d+lc, x) for some lc.– This assumes the all demand shifts are city

specific

These assumptions imply Constant Overlap

• If the share of subgroup i in the free market city A that rents apartments with k or fewer rooms is equal to the share of subgroup i in free market city B that rents apartments with n or fewer rooms, then for any other subgroup j, the share renting apartments with k or fewer rooms in city A must equal the share renting apartments with n or fewer rooms in city B.

Results

• In NYC, 47 percent of high school dropouts consume more rooms than people with college degrees (31.6 percent for the U.S. as a whole)

• In NYC, 45.7 percent of people in the bottom third of the income distribution consume more rooms than people in the top third– in the U.S. the number is 35.1 percent.

Full Structural Estimation

• Estimate the maximum cutoff of unobserved demand within each demographic group associated with each apartment size

• Calculate the total amount of misallocation: 20.9 % with correction for sampling error

• By comparison renters in Hartford (4 percent), Chicago, 7 percent

• Misallocation is strongest in Manhattan (26 percent) and among long term residents

Homeownership and Social Capital

• What is social capital? • One view is that it is socially-relevant human

capital that is determined by investment

– Social characteristics, including charisma, status and access to networks, that enable that person to extract private returns from interactions with others

• Social capital can be individual or aggregated up to form society-wide social capital

Is so, then usual investment models can understand this thing?

• Social capital should rise and fall over the lifecycle (it seems to)

• People in more social occupations should invest more (they do)

• People who are more patient or just invest more generally should invest more in social capital (they do using education)

• People who are more mobile will invest less

Homeownership and Social Capital

• Homeowners have more expected permanence and have a property stake in the quality of the community

• They should invest more in local public goods, at least that is one of the stated reasons for subsidizing ownership

• But how big are these effects really?

• And are the subsidies effective?

Are Homeowners Better Citizens?

• Using almost all measures of social capital, people who are homeowners are better citizens– .25 more organization, .09 knows school head, .10

knows US representative, .15 votes in local elections– Also, .12 garmed and .1 owns a gun– ½ of the good effects are related to permanence

• These effects however are much bigger without controls, because homeowners are really different based on observables

• The selection problem is huge

The Endogeneity Problem

• Unsolved but two approaches– first use area averages based on structures– same basic results

• Second use GSOEP data from Germany where you have a panel– Much smaller impacts in general– With fixed effects home repair drops from .12 to .09– Volunteering drops from .033 to .013 and poltiical

participation from .04 to .008– Effects are small but significant statistically

But does the subsidy do anything?

• Homeownership is essentially determined by structure– 85 percent of people in houses are owners, 85 percent of people in apartments are renters– Incentive Problems

• Homeownership doesn’t change much over time at all even though subsidy changes with inflation

• Across people are well, the size of the subsidy doesn’t seem to matter

Cities and Governments

• National governments play a huge role in shaping cities– Large scale infrastructure spending– Place based initiatives and redistribution– Transport technologies

• Local governments are also critical– Schools, Safety, Other Services– Local Redistribution

Trade and Circuses: Mega-Cities

• What determines the level of primacy across countries?

• Krugman and Livas point to international trade– because trade is space neutral (is it?) the incentive agglomerate declines

• High internal transport costs is presumably another reason to agglomerate

The Political Roots of Agglomeration

• A dictator’s desire to invest may decline with distance from him – Investment for consumption reasons– Investment to deter unrest

• Political influence declines with political distance– Physical threat declines– Lobbying, etc., also declines

• This should be more important in unstable or dictatorial regimes

Capital Cities and Transfers

• As a result, capital cities have generally received more benefits from government

• Sometimes these reflect dictators building themselves nice cities (St. Petersburg)

• Sometimes it is a response to political power of locals (Washington)

• Sometimes it is a response to local uprisings (students in Santiago)

The Empirical Causes of Mega-Cities

• Basic specification • Log(Primate City Population)=• a*Log(Non-Urban Population)• +b*Log(Urban Population)+• Country Factors • Countries with higher levels of trade do indeed

have smaller central cities (-.6)• Internal investment in roads matters (but what

about causality)

Politics is very significant

Stable Democracies

Primacy=.23 (.03)

N=24

Stable Dictatorships

Primacy=.3 (.03)

N=16

Unstable Democracies

Primacy=.35(.07)

N=6

Unstable Dictatorships

Primacy=.37 (.02)

N=39

In regressions:

• Capital City Effect = .42 (only 8 non-capitals)

• Dictatorship Effect= .44 (.15)

• Dictatorship + Instability + Interaction yields .7, 2.3 and -2.3 all significant

• But does primacy lead to dictatorship or dictatorship to primacy

Tests for Causality

• Instrument using various political variables such as ethnic heterogeneity (predates the cities) and neighboring instability-- .5

• Between 1970 and 1986, dictatorships in 1970 had faster growth in the primate city

• However, there is no significant relationship between size of capital city and becoming a dictatorship

History

• Rome’s growth peaked between 135 and 50 b.c.e. when it grew from 375k to 1,000,000.

• Strength abroad and weakness at home leades to redistribution to the capital

• Empire expanded in Gaul, Bithynia, Pontus, Cilicia and Syria

• Pompey declares all conquests are part of the city governemnt

• Sempronian and Clodian laws extrend the grain distribution to Italians in Rome

• Sulla extends citizenship to all inhabitants of Italy

Other Cities

• Edo (Tokyo) expands from little to between 500k + 1 million in the 1600s

• Growth entirely related to being Shogunal capital for newly unified Japan

• Buenos Aires grew most between 1870 and 1914– industry and politics (London)

• Paris and Mexico city are more overtly political

Local Governments

• There is a strong traditional from Tiebout (and the Federalist papers) that suggests that many benefits of local governments

• Opportunities for variety• An ability to influence outcomes through

both voice and exit• However, local governments are

particularly bad at redistribution because of mobility

Incentives and Local Governments (Public Choice, 1996)

• Inducing Local Governments to behave well presumably requires incentives

• Taxes can provide those incentives is governments want more revenue

• Property taxes have the benefit of inducing long time horizons for governments

• Tradeoff between income and property taxes involves the elasticity of demand for space (highly elastic– income tax looks better– inelastic– housing is better)

General Redistribution Point

• If the average tax rate (pure redistribution) is determined by the income level of the median voter t(y) and

• The income level of the median voter is determined by the level of redistribution y(t)

• Then the local equilibrium is determined by the point where y(t(x))=x

Median Income in the City

Tax

Rat

e fo

r R

edis

trib

utio

n

Tax Rate as a function of Income

Income as aFunction of Tax rate

The Curley Effect

• Tiebout suggests that since localities will want their communities to grow, this will create good incentives for governments

• But what if governments don’t want their cities to grow (as in zoning)

• Or even worse, what if they want their cities to lose their richest residents

Shaping the Electorate

• James Michael Curley was the mayor of Boston on four separate occasions from before WWI to after WWII

• He was highly focused on ethnic conflict and also ended up in jail

• When asked in WWI, if a UK recruiter could recruit Bostonians of British extraction to fight, Curley replied: “Go Ahead, Take Every Damn One of Them”

Curley’s Logic

• The rich anglo-bostonians were never going to vote for him

• As a result, by eliminating them he increased his vote share

• This requires some form of group identification

• This can also be seen in the policies of African-American mayors like Berry or Coleman Young (Detroit)